Ensuring Fairness in Lending: Deep Learning Approaches for Equitable Approval Prediction

Loan approval prediction plays a pivotal role in financial institutions by enabling data-driven evaluation of applicants while minimizing bias and operational delays. This paper presents a hybrid machine learning framework that integrates Artificial Neural Networks (ANN) and Support Vector Machines (SVM) to improve the accuracy and fairness of loan approval decisions. The proposed system preprocesses raw financial data through cleaning, categorical encoding, and feature scaling before feeding it into both models. The ANN is designed with multiple dense and dropout layers to prevent overfitting, while the SVM utilizes kernel functions for nonlinear classification. Comparative analysis between ANN and SVM demonstrates that the hybrid approach effectively enhances prediction reliability, ensuring equitable lending practices. The developed system is deployed through a Flask-based web interface, allowing real-time prediction and user interaction. Experimental results confirm that combining deep learning and traditional machine learning improves classification performance, making the proposed system a valuable decision-support tool for modern financial institutions.